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Bipartite output consensus of heterogeneous linear multi-agent systems by dynamic triggering observer.

Jie Duan1, Huaguang Zhang1, Ji Han1

  • 1School of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

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|March 10, 2019
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Summary

This study addresses bipartite output consensus in heterogeneous linear multi-agent systems (HL-MASs) with cooperative and antagonistic interactions. A novel observer and monitoring scheme reduce communication costs while ensuring system stability and excluding Zeno behavior.

Keywords:
Bipartite output consensusDynamic event triggering mechanismZeno behaviour

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Area of Science:

  • Control Systems Engineering
  • Networked Systems Theory
  • Robotics

Background:

  • Investigates bipartite output consensus in heterogeneous linear multi-agent systems (HL-MASs).
  • Addresses systems with both cooperative and antagonistic interactions between agents, a complexity beyond typical consensus problems.

Purpose of the Study:

  • To design a novel distributed dynamic triggering observer for leader state recovery.
  • To develop a monitoring scheme to reduce communication costs by avoiding continuous condition verification.
  • To solve the bipartite output consensus problem using state and output feedback control laws.

Main Methods:

  • Design of a distributed dynamic triggering observer.
  • Implementation of a monitoring scheme for dynamic event triggering.
  • Application of state and output feedback control strategies.
  • Analysis of the dynamic event triggering condition to ensure stability and exclude Zeno behavior.

Main Results:

  • Successfully recovered the leader's state using the designed observer.
  • Reduced communication costs through the proposed monitoring scheme.
  • Achieved bipartite output consensus in HL-MASs via feedback control laws.
  • Demonstrated that the lower bound of inter-execution time is positive, excluding Zeno behavior.

Conclusions:

  • The proposed methods effectively solve the bipartite output consensus problem for HL-MASs with complex interaction dynamics.
  • The dynamic event triggering mechanism enhances efficiency by reducing communication load without compromising stability.
  • The study provides a robust framework for consensus problems in networked systems with mixed interactions.